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This paper develops a real asset transaction approach for estimating the cost of capital for rural telephone companies whose financial assets are not publicly traded. The transaction approach uses the actual purchase prices of rural local exchange carriers (RLECs)' properties and cash flows for estimating the rate of return required by buyers and sellers of RLEC properties. The transaction approach produces higher cost of capital estimates than a traditional approach using a weighted average of debt and equity costs of proxy companies traded on organized exchanges. The estimated difference is in line with the risk premium estimated for small non-traded companies estimated by Duff and Phelps Ibbotson.

Since economic analysis of the workings of the online economy is in its early stages, detecting anticompetitive behavior remains challenging. There have been some insights from models that explain how two-sided markets work, but the practical uses of these models are limited thus far. More research is necessary on the definition and operations of platforms of different sizes and with different objectives, for example, the identification of data clustering and flows related to product clustering and information production and the relationships between data, information, and innovation. Furthermore, corporate culture, which can produce cultural clashes within an organization, may influence both (anti)competitive behavior and innovation. The cultural issue becomes even more complex when evaluating whether a merger would result in both innovation and the abuse of market power. This paper develops an overview of the technology and operations of the online economy as a start toward informing competition and antitrust policy. We present a technical overview that becomes a starting point for understanding potential areas of excessive market power. We also examine market dynamics from the large platforms' points of view to understand where they believe the online economy is heading.

Successful application of theory to practice requires good data. Yet, data can vary widely in quality, consistency, and even longevity of a particular data source. Measurement techniques can change, thus changing the definition of a data series, perhaps because of a business reorganization. In part because of industry change caused by divestiture, there were several alternative sources of data on minutes of telephone demand during the time period covered by this study. A decision had to be made which series, or what combination, to use for forecasting access rates. A canonical correlations model was used to test the similarity of two competing demand series. The number of statistically significant linear combinations indicates the number of demand series that are statistically distinct. If only one linear combination is significant, the canonical correlation algorithm will produce one optimal linear combination of these series to measure demand. The exogenous variables were the ones chosen by the FCC for its aggregate demand model. They included: trend, seasonal dummies, lagged dependent variable, and subscriber lines. The two competing demand series were the dependent variables. Results of the canonical correlation estimation showed two significant linear combinations, showing that the two competing demand series were statistically distinct and indicating that they did not both measure the true demand series. Separate regressions of each demand series on the set of exogenous variables showed that one of them was predominantly explained by the lag of its own dependent variable; the other showed marked seasonality. A comparison with predivestiture data supported the seasonal pattern. As a result of this research, the latter series was considered the more realistic measure of demand. This methodology could be applied similarly where, as was the case with divestiture in telecommunications, organizational discontinuity produces competing data series.

Digital Mosaic Pollution (DMP) arises when bits of information originally gathered for other purposes escape their original boundaries. The metaphor of a mosaic implies that bits of information, each not valuable by itself, when pieced together can create a clear, or distorted, picture of a person, depending on the collector's aims. The pollution metaphor explains how the effluent data becomes available for other purposes, including unintended and unwanted aggregation into personal digital mosaics, without consent or even awareness. Privacy invasion is an unwelcome byproduct. Much like pollution, unwanted data exposures are difficult or impossible to make private again. To gauge societal damage from information pollution, we consider privacy from a variety of academic perspectives: ethics, economics, law and regulation. The pollution analogy focuses regulatory policy and legal strategies for protecting privacy on the creators of Digital Mosaic Pollution where it can be most efficient and effective.